AI Data Scientist

At Conga, we’ve built a community where our colleagues can thrive. Here you’ll find opportunities to innovate and support growth through individual and team development, all within an environment where every voice is heard.

Conga lines up commercial operations so companies run as connected, smarter businesses. By unifying the people and processes that drive commerce, Conga aligns pricing, quoting, contracting, rebates, and communications so teams stay in sync and buyers keep moving forward. The result is trusted decisions, consistent buyer experiences, and profitable growth. More than 10,000 customers worldwide, including over 50% of the Fortune 100®, trust Conga when commercial complexity is high and global impact is on the line.

• Research and evaluate emerging NLP, machine learning, deep learning, and Generative AI techniques, translating the most promising advances into practical solutions.
• Investigate ambiguous technical problems independently, form hypotheses, and run experiments that compare competing approaches.
• Continuously evaluate new models, frameworks, and methodologies, adopting those with demonstrated potential to improve product capabilities and system performance.

• Design and build traditional NLP, statistical ML, deep learning, and transformer-based solutions using the right algorithms, architectures, and representations for the problem.
• Develop data pipelines, NLP preprocessing, feature representations, embeddings, and training datasets across structured and unstructured data.
• Establish data-quality strategies that keep models reliable as data and use cases evolve.

• Develop and evaluate LLM and GenAI solutions using prompting, RAG, embeddings, vector search, fine-tuning, agents, and structured generation.
• Select and optimize models based on accuracy, latency, cost, and business requirements.
• Help define how Conga builds, grounds, and governs GenAI capabilities across the platform.

• Design rigorous experiments, benchmarks, evaluation datasets, and statistical methodologies to measure and improve NLP, ML, and GenAI systems.
• Lead error analysis and performance measurement that turn results into clear next steps.
• Set evaluation standards the broader team can adopt and build on.

• Take NLP and AI solutions from research and prototype through production deployment, monitoring, and ongoing optimization.
• Partner with engineering teams to operationalize models at scale with strong reliability and performance.
• Proactively identify model drift, quality gaps, and optimization opportunities in live systems.

• Collaborate with engineering, product, and data science teams to solve complex problems and shape technical direction.
• Provide technical guidance and mentorship in NLP, ML, experimentation, and GenAI.
• Foster a culture of curiosity, scientific rigor, and continuous learning.

• Master's degree in Computer Science, Data Science, Computational Linguistics, Statistics, Mathematics, Engineering, or a related quantitative field. Ph.D. preferred but not required.
• 10+ years of professional experience in data science, machine learning, NLP, or AI, including significant hands-on experience building and deploying NLP and/or GenAI solutions.
• Proven experience taking ML, NLP, or GenAI solutions from experimentation through production deployment and ongoing optimization.
• Demonstrated ability to translate open-ended research into practical, measurable outcomes.
• Excellent communication skills and a track record of working effectively as a senior individual contributor across technical teams.

• Expert-level Python with extensive use of Hugging Face Transformers, spaCy, NLTK, Sentence Transformers, PyTorch, and TensorFlow.
• Expert knowledge of traditional NLP, including tokenization, stemming, lemmatization, n-grams, TF-IDF, topic modeling, text classification, named entity recognition, information extraction, semantic similarity, and information retrieval.
• Strong understanding of deep learning architectures, including CNNs, RNNs, LSTMs, GRUs, encoder-decoder models, attention mechanisms, and Transformers.
• Strong understanding of Transformer-based language models, including embeddings, pretraining, fine-tuning, and inference.
• Hands-on experience with LLMs and foundation models, including prompt engineering, RAG, vector search, reranking, fine-tuning, LoRA/QLoRA, structured generation, and LLM evaluation.
• Strong grasp of experimental design, statistical testing, cross-validation, hyperparameter optimization, and error analysis.
• Experience with NLP and GenAI evaluation metrics, including precision, recall, F1, ROC-AUC, MAP, MRR, NDCG, semantic similarity, groundedness, and relevance.
• Strong SQL skills and experience with structured and unstructured data at scale.
• Experience with distributed data processing platforms such as Spark, Databricks, or BigQuery.
• Experience with cloud platforms such as Databricks, GCP, AWS, or Azure and modern ML infrastructure.
• Experience with ML tooling such as MLflow, Kubeflow, Weights & Biases, Docker, Kubernetes, or Ray.
• Experience with vector databases and search technologies such as FAISS, Elasticsearch/OpenSearch, Pinecone, Weaviate, or Milvus.
• Familiarity with LLM application frameworks such as LangChain, LlamaIndex, or DSPy.

You bring strong theoretical foundations and the curiosity to explore new techniques, while staying grounded in what will actually move the product forward.

You enjoy getting hands-on, turning prototypes into production systems, and owning outcomes well past the first experiment.

You hold yourself and your team to high standards for evaluation, measurement, and evidence-based decisions.

You partner effectively across functions and explain complex technical concepts clearly to diverse audiences.

You elevate those around you through mentorship, thoughtful guidance, and sound technical judgment.

• Experience with NLP in the legal domain, such as NER, document classification, or semantic search over legal documents.
• Experience with advanced information retrieval, semantic search, document intelligence, multimodal models, or knowledge graphs.
• Experience with parameter-efficient fine-tuning, model quantization, distillation, inference optimization, synthetic data generation, and agentic architectures.
• Advanced knowledge of cloud AI/ML platforms and distributed training and inference for large-scale LLM systems.
• Contributions to the field through publications, patents, open-source projects, conference talks, or technical writing.
• Experience working within Agile, cross-functional product development teams.

Limited travel to other Conga offices upon request.

If this sounds like the kind of job you would love in the kind of environment where you would thrive, please click apply. We'd love to hear from you! 

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Studies have shown that women and members of ethnic minorities are less likely to apply to jobs unless they meet every single qualification. At Conga we are dedicated to building a diverse, inclusive, and authentic workplace, so if you’re excited about this role but your experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You just might be the right candidate for this or other roles.  

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